FPT-spike: a flexible precise-time-dependent single-spike neuromorphic computing architecture

Abstract

Modern Artificial Neural networks (ANNs) like Convolutional Neural Network (CNN), have found broad applications in real-world cognitive tasks. One challenging faced by these models is their tremendous memory and computing resource requirement. This also greatly hinders their adoptions from resource-constraint platforms, such as drone, mobile phone and IoT devices. Recently the brain-inspired spiking neural network (SNN) has been demonstrated as a promising solution for delivering more impressive computing and power efficiency. For SNNs, a large body of prior work were conducted on the spiking system design with a focus on using the spike firing rate (or rate-coded) for fulfilling the practical cognitive tasks. Such rate-based designs can underestimate the energy efficiency, throughput and system flexibility of SNNs. On the other hand, the potentials of time-based SNN are not fully unleashed in real applications due to lack of efficient coding and practical learning schemes in temporal domain. In this work, we make an early attempt to fill this gap: that said, a flexible precise-time-dependent single-spike neuromorphic computing architecture, namely “FPT-Spike”, is developed. “FPT-spike” relies on three hardware-favorable components: precise ultra-sparse spike temporal encoding, efficient supervised temporal learning and fast asymmetric decoding, to realize flexible spatial–temporal information trade-off for neural network size reduction without scarifying data processing capability. Extensive experimental results show that “FPT-Spike” outperforms rate-based SNN and ANN significantly in three aspects: network size, processing speed and power consumption, demonstrating great potentials for its deployment in edge devices.